Data communication and interactive visualization

Course recap

• Thus far: we’ve focused on exploratory data analysis, which involves data wrangling, summarization, and visualization
• Your data analysis journey shouldn’t stop here! Topics to consider:
  • Explanatory vs. exploratory visualization
  • Statistics and data science
  • Data ethics and “big data”

Communicating with data

• Once you’ve done all of the hard work wrangling your data, you’ll want to communicate insights to others!
• This might include:
  • Polished data products or reports
  • Models that can scale your insights

Explanatory visualization

• We’ve largely worked to this point with exploratory visualization, which refers to internally-facing visualizations that help us reveal insights about our data
• Often, externally-facing data products will include explanatory visualization, which include a polished design and emphasize one or two key points

Infographics

Obesity infographics:

• Example #1
• Example #2

Are infographics useful?

• XKCD: https://xkcd.com/1273/

Interactive reports

• Example: a data journalism article - or your Jupyter Notebook!
• Key distinction: your code, data exploration, etc. will likely be external to the report (this can vary depending on the context, however)

Data storytelling

Why interactive visualization?

• User is an active participant rather than a passive observer
• Key points to consider:
  • What are you visualizing?
  • What is the purpose of your visualization?
  • Who is your audience?
  • In what format and venue will you be presenting the visualization?

Interactive data journalism

Interactivity in the Notebook

• The ipywidgets package allows you to build basic graphical user interfaces (GUIs) to explore your data in the Notebook
• How it works: supply a function to the interact function in ipywidgets

from ipywidgets import interact

def add_five(x): 
    print(x + 5)

interact(add_five, x = (1, 100))

Interactivity with plots

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

mx = pd.read_csv('http://personal.tcu.edu/kylewalker/mexico.csv')

cols = ['mus09', 'pri10', 'sec10', 'ter10', 'gdp08']

def make_plot(x, y, title): 
    sns.lmplot(data = mx, x = x, y = y)
    plt.title(title)    
    
interact(make_plot, x = cols, y = cols, title = "Enter a plot title!")

Widgets: not just for plots!

def get_accidents(street_name): 
    part1 = 'https://data.fortworthtexas.gov/resource/kr8h-9zxd.json?streetname='
    api_call = part1 + street_name
    return pd.read_json(api_call)

def get_street(street = ['BERRY', 'UNIVERSITY', 'ROSEDALE']): 
    df = get_accidents(street)
    return df.head()
    
interact(get_street) 

Ipywidgets fundamentals

Arguments used in interact() correspond to the following:

• A boolean (True or False) returns a checkbox
• A string returns a text input
• A number (integer or float) returns a slider
• A tuple of numbers (start, end, step size) returns a range slider
• A list returns a drop-down menu

Experiment with the functions you’ve written this semester!

Interactive visualization and the web

• Analytical workflows have become increasingly connected to the web - or even completely web-based
• Interactive data visualization: graphics not just on the web, but of the web

Filtering & highlighting

Linked charts

Interactive visualization in Python

• Altair

• Bokeh

• Plotly

To use these libraries in CoCalc, the Classical Notebook, not the default CoCalc notebook, must be used

Altair example

import altair as alt

alt.Chart(mx).mark_circle(size = 60).encode(
    x = "pri10",
    y = "mus09",
    tooltip = ["name", "mus09", "pri10"]
).interactive()

Plotly example

• Plotly can convert existing matplotlib or seaborn plots to interactive charts
• The seaborn styling will not always carry over perfectly

import plotly.offline as py
py.init_notebook_mode()

sns.scatterplot(data = mx, x = 'pri10', y = 'mus09')
fig = plt.gcf() # "Get current figure"
py.iplot_mpl(fig)

Tableau

• Highly popular software for data visualization - both exploratory and explanatory
• Intuitive, drag-and-drop interface
• Key feature: the dashboard

Data dashboards

• Tableau public gallery
• Integration with Mapbox

Demo: Tableau Public

• Let’s work with a (cleaned) obesity dataset you’ll be familiar with from earlier in the semester; download it at http://personal.tcu.edu/kylewalker/data/overweight.csv